数字化dm:制造业持续数字化的可持续数据挖掘模型

Christian Weber, P. Czerner, M. Fathi
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摘要

制造业作为一个行业面临着持续的压力,要求以正确的质量、数量和时间交付正确的产品。为了做到这一点,在短时间内发现制造问题的根源以及防止已知问题的进一步发生变得越来越重要。数据挖掘的重点是识别问题模式并推断出正确的解释,从而及时跟踪和解决问题的根本原因。然而,吸取的经验教训很少被传输到数字解决方案中,然后彻底实现自动检测和解决事件。数据挖掘模型已经存在,但没有结构化的方法来数字化地转换和维持已发现的解决方案。我们正在引入digital - dm作为数字化分析结果的结构化和战略性过程。digital - dm是建立在现有数据挖掘模型之上的,但它定义了一个持续数字化的战略过程,利用分析经验教训,实现可持续的数字化制造支持。
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Digit-DM: A Sustainable Data Mining Modell for Continuous Digitization in Manufacturing
Manufacturing as an industry is under continuous pressure to deliver the right product, at the right quality, quantity and in time. To do so it becomes increasingly important to detect the source of manufacturing problems in a short amount of time but also to prevent further occurrence of know problems. Data Mining is focused on identifying problem patterns and inferring the right interpretation to trace and resolve the root cause in time. However, lessons learned are rarely transported into digital solutions that then thoroughly enable to automatize detection and resolving of incidents. Data mining models exist, but no structured approach for transforming and sustaining found solutions digitally. We are introducing Digit-DM as a structured and strategic process for digitizing analytical results. Digit-DM is building on top of existing data mining models but defines a strategic process for continuous digitization, enabling sustainable, digital manufacturing support, utilizing analytical lessons learned.
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